Comparing supervised classification algorithm–feature combinations for Spartina alterniflora extraction: a case study in Zhanjiang, China by Qiujie Chen, Qiujie Chen, Chunyan Shen, Hong Du, Danling Tang

“…Mangrove forests are vital blue carbon ecosystems whose security is increasingly threatened by the non-native species Spartina alterniflora. …”
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A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs by Alireza Mahmoudian, Mussa Mahmoudi, Mohammad Yekrangnia, Nima Tajik, Mostafa Mohammadzadeh Taleshi

“…The efficacy of the approach is showcased using the Random Forest Regressor model, finely tuned through a Grid Search technique, with performance evaluated using the R-squared coefficient and Root Mean Squared Error metrics. …”
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Artificial intelligence-driven near-infrared spectrophotometry model for rapid quantification of anti-nutritional factors in soybean (Glycine max.) by Norberto Jose Palange, Tonny Obua, Julius Pyton Sserumaga, Enoch Wembabazi, Mildred Ochwo-Ssemakula, Ephraim Nuwamanya, Isaac Onziga Dramadri, Moses Matovu, Richard Edema, Phinehas Tukamuhabwa

“…Predictive models were developed through partial least squares (PLS) and random forest (RF) regressions. The random forest models outperformed partial least squares regression with the best predictive performance of R2 test = 0.97; RPD = 5.95 and R2 test = 0.96; RPD = 3.62 for phytate and TTI, respectively. …”
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Improved estimation of stomatal conductance by combining high-throughput plant phenotyping data and weather variables through machine learning by Junxiao Zhang, Kantilata Thapa, Geng (Frank) Bai, Yufeng Ge

“…Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for gs, and model performance was evaluated by Coefficient of Determination (R2) and Root Mean Squared Error (RMSE). …”
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Wastewater-based epidemiology of influenza A virus in Shenzhen: baseline values and implications for multi-pathogen surveillance by Xiuyuan Shi, Shisong Fang, Chen Du, Guixian Luo, Yanpeng Cheng, Zhen Zhang, Qiuying Lv, Xin Wang, Zhigao Chen, Bincai Wei, Ziqi Wu, Bingchan Guo, Panpan Yang, Miaomiao Luo, Weihua Wu, Liping Zhou, Ting Huang, Xuan Zou, Xiaolu Shi, Songzhe Fu, Zhanwei Du, Xinxin Han, Yinghui Li, Qinghua Hu

“…The optimized random forest model (mean absolute error = 2,307, R2 = 0.988) integrated IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen, and phosphorus. …”
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Estimation of soil temperature for agricultural applications in South Africa using machine-learning methods by Lindumusa Myeni, Tlotlisang Nkhase, Ramontsheng Rapolaki, Zaid Bello, Mokhele E. Moeletsi

“…The results showed that soil temperature at various depths can be reasonably estimated by different generic machine-learning models, with average Nash–Sutcliffe efficiency values ranging from 0.74 for decision tree to 0.87 for random forest models and root mean square error values of less than 2.79 °C for all models. …”
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Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques by Isteaq Kabir Sifat, Tajin Ahmed Jisa, Jyoti Shree Roy, Nourin Sultana, Farhana Hasan, Md Parvez Mosharaf, Md. Kaderi Kibria

“…ABSTRACT Background and Aims Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. …”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…To address this challenge, our research develops an innovative UAV-based monitoring framework through systematic integration of long-term multispectral/thermal infrared observations with multi-model fusion: (1) Surface Energy Balance Algorithm for Land (SEBAL) and FAO-56 Penman-Monteith models for evapotranspiration (ET) estimation; (2) Random Forest algorithm incorporating four phenotypical growth indicators for yield estimation, ultimately enabling CWP quantification. …”
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GNSS-R-Based wildfire detection: a novel and accurate method by Xuke Wang, Wei Yao

“…In recent years, global forests have faced frequent wildfires due to climate change, leading to significant ecological and economic losses. …”
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Hydrological and hydrodynamic coupling simulation under composite underlying surfaces in urban polder areas by Cheng Chen, Binquan Li, Yang Xiao, Huihui Li, Taotao Zhang, Dong Xu, Huanghao Yu

“…A BP neural network (BPNN) was employed for error correction to reduce model uncertainty in forecasting. …”
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Leveraging Machine Learning for Exchange Rate Prediction: A Business and Financial Management Perspective in Nigeria by Adedeji Daniel Gbadebo

“…Findings The findings indicate that the Random Forest (RF) model outperforms other approaches in predicting Nigeria’s exchange rate against the US dollar, demonstrating the lowest prediction errors (MAE, MSE, RMSE, and MAPE). …”
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Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features by Xiuyi Li, Yue Zhou, Weiwei Zhao, Chuanyun Fu, Zhuocheng Huang, Nianqian Li, Haibo Xu

“…RF also performs well as per the RMSE metric, as it is suitable for predicting landing position errors of outliers.…”
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Bridging the Gap Between Machine Learning and Medicine: A Critical Evaluation of the Dworak Regression Grade in Rectal Cancer by Camille Raets, Chaimae El Aisati, Amir L. Rifi, Mark De Ridder, Koen Putman, Johan De Mey, Alexandra Sermeus, Kurt Barbe

“…This study investigated the consistency between Random Forest’s prediction for rectal cancer regression grades and doctors’ opinion based on clinical data. …”
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Integrating Temporal Vegetation and Inundation Dynamics for Elevation Mapping Across the Entire Turbid Estuarine Intertidal Zones Using ICESat-2 and Sentinel-2 Data by Siqi Yao, Jianrong Zhu, Wanying Zhang, Bo Tian, Weiwei Sun, Weiguo Zhang, Weiming Xie, Pengjie Tao, Chunpeng Chen, Kai Tan

“…This method utilizes a random forest (RF) to model the relationships between elevations from Ice, Cloud, and Elevation Satellite 2 (ICESat-2) and band, texture, and index features from Sentinel-2, without relying on any supplementary in situ measurements. …”
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High resolution soil moisture mapping in 3D space and time using machine learning and depth functions by Mo Zhang, Yong Ge, Gerard B.M. Heuvelink, Yuxin Ma

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Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model by Yaoping WANG, Te LI, Kaihua JIANG, Wenhui LI, Qiang WU, Yu WANG

“…The results show that the average absolute error of ESDD prediction of AdaBoost-MEA-ELM model is 0.0032 mg/cm2, which is 58.97% lower than that of the original ELM model. …”
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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

“…The models’ accuracies were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R2), Mean Bias Deviation (MBD), and Willmott’s Index of Agreement (d). …”
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Prediction rotary drilling penetration rate in lateritic soils using machine learning models by Eugène Gatchouessi Kamdem, Franck Ferry Kamgue Tiam, Luc Leroy Mambou Ngueyep, Olivier Wounabaissa, Hugues Richard Lembo Nnomo, Abraham Kanmogne

“…The key performance metrics including correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. …”
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Performance prediction using educational data mining techniques: a comparative study by Yaosheng Lou, Kimberly F. Colvin

“…The results indicated that generalized linear regression consistently outperformed decision tree and random forest regression in terms of both predictive accuracy and error. …”
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A Measure–Correlate–Predict Approach for Transferring Wind Speeds from MERRA2 Reanalysis to Wind Turbine Hub Heights by José A. Carta, Diana Moreno, Pedro Cabrera

“…To address this challenge, we propose a reproducible Measure–Correlate–Predict (MCP) framework that integrates Random Forest (RF) supervised learning to estimate hub-height wind speeds from MERRA2 data at 50 m. …”
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